A Computational Pipeline to Improve Clinical Alarms Using a Parallel Computing Infrastructure

Nguyen, Andrew V.

ProQuest LLC, Ph.D. Dissertation, University of California, San Francisco

Physicians, nurses, and other clinical staff rely on alarms from various bedside monitors and sensors to alert when there is a change in the patient's clinical status, typically when urgent intervention is necessary. These alarms are usually embedded directly within the sensor or monitor and lacks the context of the patient's medical history and data from other sensors. A missed alarm may result in severe morbidity or mortality so alarm algorithms tend to err on the side of sensitivity. As a result, 40-90% of alarms within the clinical setting are false. These false alarms have a negative impact on patient care as clinicians become desensitized to the alarms. False alarms also directly impact patient recovery due to sleep interruption and increased anxiety. There is an increasing amount of research dedicated to improving clinical alarms but the field lacks a standardized approach to capturing, storing, processing, and analyzing physiological sensor data. This work focuses on the informatics issues and demonstrates a standardized platform for conducting research involving the combination of various clinical data sources (e.g. EHR/EMR) with physiological sensor data (including high resolution waveforms). This platform can also be easily extended into the clinical environment to eventually provide clinical decision support. The standardized approach presented has the additional benefit that researchers do not have to concern themselves with how to connect, retrieve, or format the data. They can focus solely on the higher level problems of designing the experiments, implementing algorithms, and interpreting results. A 3-stage computational pipeline was implemented on top of a Hadoop-based parallel computing platform to reduce the number of false alarms of ventricular fibrillation and ventricular tachycardia. Two waveforms (ECG and arterial blood pressure) were fed through several feature extraction and change (point) detection algorithms, then through supervised learning algorithms to generate a model that was able to better detect true alarm situations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]